Investing in an AI startup requires more than checking a demo and a growth chart. Verify the legal entity and ownership, customer evidence, repeatable model performance, data and intellectual-property rights, security controls, regulatory exposure, team capacity, financial records, and the exact financing terms. Use primary documents and reproducible tests; treat unsupported claims as unverified until the company supplies evidence.
This checklist gives you a practical diligence sequence, the records to request, tests to run, red flags that should pause a deal, and a consistent way to compare two or more AI companies.
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Start with a written diligence plan
Before requesting files, define the investment thesis in falsifiable terms. Write down the customer problem, buyer, expected price, deployment model, target gross margin, technical performance required, jurisdictions involved, and the milestones that must be reached before the next financing. For each claim, specify acceptable evidence, who will provide it, and what result would change your decision.
Use one evidence template for every company. Keep a source register for contracts, test runs, financial records, and legal opinions. Record document dates, versions, scope, and any limitations. A secure investor data room can keep sensitive records organized, but access controls and an audit trail matter more than the product label.
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1. Verify the company, ownership, and deal
Confirm the legal entity and capitalization
Request the certificate of incorporation, current good-standing evidence, tax registration where applicable, organizational chart, and the entity’s registered address. Reconcile the entity receiving your funds with the entity that owns the intellectual property and signs customer contracts. A group structure that leaves core IP or revenue in another entity changes your risk.
Obtain a fully diluted capitalization table dated for the proposed closing. It should identify founders, employees, contractors, investors, option pools, warrants, SAFEs, convertible notes, side letters, debt, and any promised but undocumented equity. Reconcile every entry to board approvals, subscription agreements, transfer records, and prior closing statements. Ask for board and shareholder minutes covering financings, option grants, related-party transactions, and material litigation.
Check founder and contributor rights
Every founder, employee, contractor, and material technical contributor should have signed confidentiality and invention-assignment agreements covering the work used by the startup. Check prior-employer obligations and consulting agreements for restrictions that could conflict with the company’s product. Missing assignments can leave ownership disputed even when code and models are in the company’s repositories.
Request a schedule of patents, patent applications, trademarks, copyrights, trade secrets, domain names, licenses, open-source components, model weights, APIs, and foundation-model terms. For each item, note the owner, territory, expiry or renewal date, permitted commercial uses, attribution duties, copyleft obligations, and termination rights. Ask management to identify any component that cannot be replaced if a license changes.
Read the security and rights you are buying
Obtain the exact term sheet and definitive security documents. Review valuation, liquidation preference, participation, pro rata rights, anti-dilution, conversion mechanics, option-pool treatment, information rights, protective provisions, board rights, transfer restrictions, debt, and use-of-proceeds covenants. Compare those rights with the company’s actual risk and evidence; a high growth claim does not cure unclear ownership or unfavorable control terms.
2. Test the customer problem and traction
Define who pays and why
Identify the economic buyer, daily user, affected third parties, procurement gatekeepers, and the cheaper substitutes, including manual work and conventional software. Document the customer’s measurable outcome: time saved, revenue gained, error reduction, risk avoided, or another result that supports payment. Ask how long a sale takes, what security review is required, and what implementation work the startup performs.
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Reconcile reported revenue
Request customer contracts, statements of work, invoices, cash receipts, renewal records, churn reports, cohort tables, usage logs, and a pipeline export. Reconcile reported ARR or revenue to the general ledger, bank statements, and accounting system. Separate signed recurring contracts from pilots, free trials, one-time services, founder-led projects, non-binding letters, and usage that has not yet generated cash.
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Call a representative sample of customers, including at least one that did not renew if available. Ask what was purchased, which outcomes were achieved, who operates the system, what happens when the model changes, and whether the customer would pay the current price without founder involvement. Test retention after price increases, model-version changes, procurement demands, and periods of degraded performance.
Calculate real unit economics
Rebuild gross margin using inference, storage, monitoring, human review, support, implementation, payment processing, and third-party API costs. Confirm whether cloud and model-provider bills are assigned to customer cohorts or hidden in a general expense line. Model margin at current and stressed usage levels; an apparent software margin can disappear when customers submit longer prompts, require more frequent calls, or demand human escalation.
3. Establish that the AI product works repeatedly
Request a reproducible technical package
Ask for a current system diagram, model and provider inventory, repository and deployment map, version history, evaluation protocol, representative test set, baseline comparisons, error taxonomy, latency, throughput, uptime, cost per task, and rollback procedure. Require a live or reproducible demonstration connected to the claimed customer workflow. A single polished demo cannot establish repeatability, robustness, or economics.
For every headline metric, record the model version, data snapshot, prompts, thresholds, hardware, evaluator, sample size, and date. Require results on representative and edge-case data, not only curated examples. If the company reports accuracy, ask for confidence intervals or another statement of uncertainty and the baseline against which improvement is measured.
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Run or observe tests for:
- Task success and accuracy on normal, rare, and adversarial inputs.
- False-positive and false-negative rates, with the business cost of each error.
- Calibration and uncertainty, including when the system should abstain.
- Robustness to distribution shift, missing fields, malformed input, and language or demographic variation relevant to the market.
- Prompt injection, data poisoning, privacy leakage, harmful or disallowed output, and unauthorized tool use.
- Human-review frequency, reviewer qualifications, queue times, and escalation outcomes.
Preserve the test prompts, outputs, logs, and evaluator instructions. Ask who ran each test and whether management excluded failures from the reported score. A model that performs well only with hidden manual correction has different economics and risk from an autonomous product.
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Assess dependency and portability
Map dependence on foundation-model providers, APIs, specialized hardware, data vendors, and one cloud region. Review provider terms for training on customer data, commercial use, rate limits, indemnities, geographic processing, price changes, and termination. Ask for a fallback model, portability plan, migration time, and price-sensitivity analysis. Determine which components can be rebuilt internally if a provider withdraws access or changes its license.
4. Verify data rights and provenance
Request a complete inventory covering training, fine-tuning, retrieval, evaluation, monitoring, and production data. For each dataset or stream, record source, owner, license or other legal basis, consent and notice, geographic origin, retention period, deletion process, and permitted commercial uses. Confirm that customer contracts authorize the stated training, inference, monitoring, and product-improvement activities.
“Publicly available” does not prove unrestricted training rights. Match each dataset and model component to contractual terms, privacy duties, copyright analysis, and the actual product use. Where rights are uncertain, estimate remediation cost, identify affected customers and jurisdictions, and limit the investment thesis to uses that can be documented.
Check whether deletion requests propagate through caches, vector stores, fine-tuning sets, backups, logs, and derived artifacts. Ask how the company prevents one customer’s confidential data from appearing in another customer’s retrieval results or outputs.
5. Examine security, privacy, and resilience
Request operational security evidence
Obtain threat models, penetration-test or red-team reports, vulnerability and patch records, identity and access-control policies, encryption design, secrets-management procedures, logging and alerting configuration, backup and recovery tests, incident history, breach notices, and customer security questionnaires. If the company cites SOC 2, ISO 27001, or an equivalent report, check the report’s scope, control period, exceptions, opinion, and date. A certification outside the product or period you are underwriting is not evidence for your decision.
Ask for a current asset inventory, privileged-access review, dependency-scanning results, software bill of materials where maintained, and recovery objectives. Verify that a failed model provider, cloud region, database, or queue has a tested fallback rather than an undocumented promise.
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Map privacy obligations by use case
Determine whether the product handles personal, health, financial, employment, biometric, children’s, or other sensitive data. Map controller and processor roles, subprocessors, retention and deletion, data-subject rights, breach response, cross-border transfers, and customer notification duties. Obtain qualified counsel’s analysis for every operating and customer jurisdiction. A voluntary framework or internal policy does not itself establish legal compliance.
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6. Evaluate responsible-AI governance and regulation
Find the accountable owner
Identify the person or committee responsible for AI risk and the escalation path to executives and the board. Request model-release criteria, monitoring thresholds, incident-triage procedures, customer-notification rules, remediation tracking, and explicit pause, withdrawal, or shutdown criteria. Ask for incident and complaint logs, risk registers, model cards or equivalent documentation, and evidence that controls operate in production.
Use a due-diligence cycle
The OECD’s OECD Due Diligence Guidance for Responsible AI, published 19 February 2026, provides a six-step governance spine:
- Embed responsible business conduct in policies and management systems.
- Identify and assess actual and potential adverse impacts.
- Cease, prevent, or mitigate those impacts.
- Track implementation and results.
- Communicate actions and findings.
- Provide or cooperate in remediation when harm occurs.
Ask for an owner, evidence, and review date for each step. The quoted first steps are “Embed RBC into policies and management systems” and “Identify and assess actual and potential adverse impacts.” Treat the guidance as a management method, not a substitute for local law.
Build a jurisdiction and sector map
Have counsel analyze privacy, consumer-protection, product-liability, employment, sectoral, intellectual-property, and AI-specific requirements for each use case and country or state. Pay particular attention to hiring, lending, insurance, healthcare, education, biometrics, safety-critical systems, and customer-facing decisions. Record the legal trigger, responsible entity, required notice or human review, technical control, evidence retained, and consequence of non-compliance.
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Confirm founder and key-employee identities, employment and contractor agreements, references, prior-company IP obligations, role coverage, hiring plan, and retention risk. Test whether the team can operate the model, data pipelines, security program, compliance process, and customer support it sells. A product dependent on one founder or one vendor has concentration risk that should appear in the valuation and milestones.
Tie the roadmap to measurable outcomes: customer results, quality thresholds, gross-margin targets, reliability, security remediation, regulatory approvals where applicable, and hiring. Ask what the next financing depends on and what evidence would disprove the plan. Milestones should have an owner, date, measurement method, and consequence if missed.
8. Reconcile the financial model and financing need
Request historical financial statements, bank statements, general ledger, tax filings, accounts receivable and payable aging, payroll, cloud bills, model-provider invoices, debt schedules, burn calculation, and runway forecast. Reconcile revenue and costs to source records. Challenge assumptions about sales-cycle length, conversion, retention, usage, hiring, inference prices, and support load.
Model downside cases for slower sales, lower retention, higher inference prices, a security incident, legal remediation, customer refunds, and loss of a key provider. Show the cash runway and financing requirement in each case. Ask which assumptions are contractual, which are observed, and which are management estimates.
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- Management refuses primary records or insists that a slide deck is sufficient.
- Accuracy figures lack test-set provenance, baseline, model version, error analysis, or a reproducible protocol.
- Training data, model, API, or open-source rights cannot be documented.
- Revenue cannot be reconciled to contracts, invoices, usage, renewals, or cash.
- The product depends on one provider without a fallback, portability plan, or price-sensitivity analysis.
- IP assignments are missing; cap-table entries, side letters, or informal equity promises are unexplained.
- No person owns incidents, monitoring, withdrawal criteria, or remediation records.
- Regulatory exposure is treated as a checkbox instead of a use-case and jurisdiction map.
- Security reports are stale, out of scope, or missing exceptions and remediation status.
- Customer outcomes depend on undisclosed manual work that is absent from the margin model.
10. Compare AI startups on the same evidence
Do not compare one company’s marketing metrics with another’s audited records. Give each startup the same requests, test set where feasible, interview questions, and financial definitions. Score the following dimensions using documented evidence rather than a single composite number:
| Dimension | Evidence to compare | Decision question |
|---|---|---|
| Customer pain | Buyer interviews, contracts, outcomes, substitutes | Is the problem urgent enough to support durable pricing? |
| Traction and retention | Invoices, cash, cohorts, renewals, churn, usage | Does demand persist beyond pilots and founder-led sales? |
| Technical performance | Representative tests, baselines, error costs, reliability | Does the system meet the workflow’s threshold repeatedly? |
| Economics | Inference, storage, human-review, support, and provider costs | Is gross margin resilient at realistic usage? |
| Defensibility | Proprietary data rights, workflow integration, portability analysis | What can competitors or providers replicate or remove? |
| Security and resilience | Threat model, testing, access controls, recovery evidence | Can the company protect customers and operate through failure? |
| Regulation and governance | Use-case map, accountable owner, incidents, controls | Can risks be identified, mitigated, and remedied in each jurisdiction? |
| Team execution | References, role coverage, hiring and milestone plan | Can this team deliver and support the promised system? |
| Financing terms | Valuation, preferences, dilution, rights, debt, use of proceeds | Are downside outcomes acceptable at the proposed price? |
11. Turn findings into an investment decision
Classify every open issue as material, remediable before closing, remediable after closing, or deal-breaking. For each item, record the question, evidence owner, due date, materiality, proposed covenant or condition, and the event that would change your recommendation.
- Invest: critical claims are verified, risks are controlled, and terms compensate for remaining uncertainty.
- Invest only after remediation: specify the document, test, contract change, security fix, or legal opinion required before funds are released.
- Defer pending evidence: keep the opportunity open while the company completes defined tests or reconciliations.
- Pass: ownership, rights, economics, safety, or governance cannot be made reliable at acceptable cost and timing.
Put conditions in definitive documents where possible: information rights, reporting cadence, use-of-proceeds limits, security milestones, data-rights remediation, key-person protections, and consent rights for material provider or IP changes. Revisit volatile provider terms, regulatory requirements, security status, and market conditions before investment-committee approval and again before closing.
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